Global Tracking Control for a Class of Uncertain Nonlinear SISO Systems

This paper investigates the problem of global tracking control for a class of nonlinear systems with unknown system functions. The key features of the proposed algorithm are given as follows. First, a novel nth-order smoothly switching function is presented, and therefore an energy-efficient controller is obtained. Second, only a neural network is employed to compensate for all the unknown parts in each backstepping design procedure to reduce the number of adaptive parameters, so that a more simplified controller is proposed. Finally, the overall controller ensures that all the signals in the closed-loop system are globally uniformly ultimately bounded (GUUB) and the output of the system converges to a small neighborhood of the reference trajectory by properly choosing the design parameters. A simulation example is given to illustrate the effectiveness of the proposed control scheme.